Transcription is not generation: distinguishing non-generative AI tool use from academic misconduct in higher education assessment

Since 2023, universities worldwide have adopted policies restricting or prohibiting the use of generative artificial intelligence (GenAI) in assessed work. In the United Kingdom, the Quality Assurance Agency (QAA) and the Russell Group have issued guidance, and many institutions now operate tiered assessment classification systems. Internationally, An et al. (2025) report that ninety-four per cent of the top fifty US universities (by the ranking used in that study) had issued faculty-facing GenAI guidelines, and Luo (2024) documents broadly similar structures across the world’ s top twenty institutions by QS ranking. This paper argues that such policies frequently lack the technical precision necessary to distinguish between genuinely generative AI use—where a machine produces the assessed intellectual content—and non-generative uses of AI-powered tools, such as optical character recognition (OCR), voice-to-text transcription, and handwriting recognition, where the tool performs a clerical format-conversion function on content the student has already authored. The paper is a conceptual and policy analysis; it advances interpretive arguments and a proposed evidential framework, not empirical findings. Its principal limitations are stated in Section 7.4.